Learning Curve in Left Ventricular Assist Device Implantation: Low Volumes Do Not Equate Bad Outcomes
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: Most implantations of left ventricular assist devices (LVAD) are performed in low-volume centers. This study aimed to evaluate the procedural learning curve of HeartMate II (HM2) implantations by comparing outcomes between two time periods in a low-volume center. METHODS: All 51 consecutive patients undergoing HM2 implantation between January 2009 and December 2017 were reviewed and allocated into 2 groups: early-era group (from 2009 to 2014; n=25) and late-era group (from 2015 to 2017; n=26). The primary outcome was the 90-day mortality rate, and the secondary outcome was a composite of mortality, neurological event, reoperation for bleeding, need for temporary right ventricular assist device, and pump thrombosis at 90 days. Median follow-up time was 51 months (0-136). A cumulative sum (CUSUM) control analysis was used to establish a threshold of implantations that optimizes outcomes. RESULTS: Patients in the early era had a higher rate of diabetes, previous stroke, and inotrope support before HM2 implantation. The 90-day mortality rate was not significantly higher in the early era (24% vs. 15%, P=0.43), but the composite endpoint was significantly higher (76% vs. 42%, P=0.01). The CUSUM analysis found a threshold of 23 operations after which the composite endpoint was optimized. CONCLUSION: Patients undergoing HM2 implantation in a low-volume center have improving outcomes with number of cases and optimized results after a threshold of 23 cases. Significant changes in patient selection, surgical techniques, and patient management might lead to improved outcomes after LVAD implantation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it